Multilingual Task

Multilingual task research focuses on enabling language models to effectively process and generate text across multiple languages, aiming to overcome limitations imposed by the dominance of English in training data. Current efforts concentrate on improving model performance across diverse languages, particularly low-resource ones, through techniques like instruction fine-tuning, cross-lingual transfer learning, and the development of novel multilingual datasets and evaluation metrics. These advancements are crucial for bridging the language gap in various applications, including machine translation, question answering, and sentiment analysis, ultimately fostering greater inclusivity and accessibility in natural language processing.